






"""simple docstring"""





import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import DetrConfig, DetrForObjectDetection, DetrForSegmentation, DetrImageProcessor, ResNetConfig
from transformers.utils import logging


logging.set_verbosity_info()
_SCREAMING_SNAKE_CASE   :							int          =		logging.get_logger(__name__)







def 	lowerCamelCase__			(				_lowerCamelCase			:						Tuple					) ->							Union[str, Any]:
  # initialize config
  if "resnet-50" in model_name:
    lowerCamelCase_       	=		ResNetConfig.from_pretrained('microsoft/resnet-50'					)
  elif "resnet-101" in model_name:
    lowerCamelCase_       	=		ResNetConfig.from_pretrained('microsoft/resnet-101'					)
  else:
    raise ValueError('Model name should include either resnet50 or resnet101'					)

  lowerCamelCase_       	=		DetrConfig(use_timm_backbone=_lowerCamelCase						,							backbone_config=_lowerCamelCase					)

  # set label attributes
  lowerCamelCase_       	=		'panoptic' in model_name
  if is_panoptic:
    lowerCamelCase_       	=		250
  else:
    lowerCamelCase_       	=		91
    lowerCamelCase_       	=		'huggingface/label-files'
    lowerCamelCase_       	=		'coco-detection-id2label.json'
    lowerCamelCase_       	=		json.load(open(hf_hub_download(_lowerCamelCase						,							_lowerCamelCase						,							repo_type='dataset'					)						,							'r'					)					)
    lowerCamelCase_       	=		{int(_lowerCamelCase					): v for k, v in idalabel.items()}
    lowerCamelCase_       	=		idalabel
    lowerCamelCase_       	=		{v: k for k, v in idalabel.items()}

  return config, is_panoptic







def 	lowerCamelCase__			(				_lowerCamelCase			:						str					) ->							Optional[Any]:
  # here we list all keys to be renamed (original name on the left, our name on the right)
  lowerCamelCase_       	=		[]

  # stem
  # fmt: off
  rename_keys.append(('backbone.0.body.conv1.weight', 'backbone.conv_encoder.model.embedder.embedder.convolution.weight')					)
  rename_keys.append(('backbone.0.body.bn1.weight', 'backbone.conv_encoder.model.embedder.embedder.normalization.weight')					)
  rename_keys.append(('backbone.0.body.bn1.bias', 'backbone.conv_encoder.model.embedder.embedder.normalization.bias')					)
  rename_keys.append(('backbone.0.body.bn1.running_mean', 'backbone.conv_encoder.model.embedder.embedder.normalization.running_mean')					)
  rename_keys.append(('backbone.0.body.bn1.running_var', 'backbone.conv_encoder.model.embedder.embedder.normalization.running_var')					)
  # stages
  for stage_idx in range(len(config.backbone_config.depths					)					):
    for layer_idx in range(config.backbone_config.depths[stage_idx]					):
      # shortcut
      if layer_idx == 0:
        rename_keys.append(
            (
                F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.0.weight''',
                F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.convolution.weight''',
            )					)
        rename_keys.append(
            (
                F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.weight''',
                F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.weight''',
            )					)
        rename_keys.append(
            (
                F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.bias''',
                F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.bias''',
            )					)
        rename_keys.append(
            (
                F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_mean''',
                F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_mean''',
            )					)
        rename_keys.append(
            (
                F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_var''',
                F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_var''',
            )					)
      # 3 convs
      for i in range(3					):
        rename_keys.append(
            (
                F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.conv{i+1}.weight''',
                F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.convolution.weight''',
            )					)
        rename_keys.append(
            (
                F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.weight''',
                F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.weight''',
            )					)
        rename_keys.append(
            (
                F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.bias''',
                F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.bias''',
            )					)
        rename_keys.append(
            (
                F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_mean''',
                F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_mean''',
            )					)
        rename_keys.append(
            (
                F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_var''',
                F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_var''',
            )					)
    # fmt: on

  for i in range(config.encoder_layers					):
    # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
    rename_keys.append(
        (
            F'''transformer.encoder.layers.{i}.self_attn.out_proj.weight''',
            F'''encoder.layers.{i}.self_attn.out_proj.weight''',
        )					)
    rename_keys.append(
        (F'''transformer.encoder.layers.{i}.self_attn.out_proj.bias''', F'''encoder.layers.{i}.self_attn.out_proj.bias''')					)
    rename_keys.append((F'''transformer.encoder.layers.{i}.linear1.weight''', F'''encoder.layers.{i}.fc1.weight''')					)
    rename_keys.append((F'''transformer.encoder.layers.{i}.linear1.bias''', F'''encoder.layers.{i}.fc1.bias''')					)
    rename_keys.append((F'''transformer.encoder.layers.{i}.linear2.weight''', F'''encoder.layers.{i}.fc2.weight''')					)
    rename_keys.append((F'''transformer.encoder.layers.{i}.linear2.bias''', F'''encoder.layers.{i}.fc2.bias''')					)
    rename_keys.append(
        (F'''transformer.encoder.layers.{i}.norm1.weight''', F'''encoder.layers.{i}.self_attn_layer_norm.weight''')					)
    rename_keys.append(
        (F'''transformer.encoder.layers.{i}.norm1.bias''', F'''encoder.layers.{i}.self_attn_layer_norm.bias''')					)
    rename_keys.append(
        (F'''transformer.encoder.layers.{i}.norm2.weight''', F'''encoder.layers.{i}.final_layer_norm.weight''')					)
    rename_keys.append((F'''transformer.encoder.layers.{i}.norm2.bias''', F'''encoder.layers.{i}.final_layer_norm.bias''')					)
    # decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
    rename_keys.append(
        (
            F'''transformer.decoder.layers.{i}.self_attn.out_proj.weight''',
            F'''decoder.layers.{i}.self_attn.out_proj.weight''',
        )					)
    rename_keys.append(
        (F'''transformer.decoder.layers.{i}.self_attn.out_proj.bias''', F'''decoder.layers.{i}.self_attn.out_proj.bias''')					)
    rename_keys.append(
        (
            F'''transformer.decoder.layers.{i}.multihead_attn.out_proj.weight''',
            F'''decoder.layers.{i}.encoder_attn.out_proj.weight''',
        )					)
    rename_keys.append(
        (
            F'''transformer.decoder.layers.{i}.multihead_attn.out_proj.bias''',
            F'''decoder.layers.{i}.encoder_attn.out_proj.bias''',
        )					)
    rename_keys.append((F'''transformer.decoder.layers.{i}.linear1.weight''', F'''decoder.layers.{i}.fc1.weight''')					)
    rename_keys.append((F'''transformer.decoder.layers.{i}.linear1.bias''', F'''decoder.layers.{i}.fc1.bias''')					)
    rename_keys.append((F'''transformer.decoder.layers.{i}.linear2.weight''', F'''decoder.layers.{i}.fc2.weight''')					)
    rename_keys.append((F'''transformer.decoder.layers.{i}.linear2.bias''', F'''decoder.layers.{i}.fc2.bias''')					)
    rename_keys.append(
        (F'''transformer.decoder.layers.{i}.norm1.weight''', F'''decoder.layers.{i}.self_attn_layer_norm.weight''')					)
    rename_keys.append(
        (F'''transformer.decoder.layers.{i}.norm1.bias''', F'''decoder.layers.{i}.self_attn_layer_norm.bias''')					)
    rename_keys.append(
        (F'''transformer.decoder.layers.{i}.norm2.weight''', F'''decoder.layers.{i}.encoder_attn_layer_norm.weight''')					)
    rename_keys.append(
        (F'''transformer.decoder.layers.{i}.norm2.bias''', F'''decoder.layers.{i}.encoder_attn_layer_norm.bias''')					)
    rename_keys.append(
        (F'''transformer.decoder.layers.{i}.norm3.weight''', F'''decoder.layers.{i}.final_layer_norm.weight''')					)
    rename_keys.append((F'''transformer.decoder.layers.{i}.norm3.bias''', F'''decoder.layers.{i}.final_layer_norm.bias''')					)

  # convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
  rename_keys.extend(
      [
          ('input_proj.weight', 'input_projection.weight'),
          ('input_proj.bias', 'input_projection.bias'),
          ('query_embed.weight', 'query_position_embeddings.weight'),
          ('transformer.decoder.norm.weight', 'decoder.layernorm.weight'),
          ('transformer.decoder.norm.bias', 'decoder.layernorm.bias'),
          ('class_embed.weight', 'class_labels_classifier.weight'),
          ('class_embed.bias', 'class_labels_classifier.bias'),
          ('bbox_embed.layers.0.weight', 'bbox_predictor.layers.0.weight'),
          ('bbox_embed.layers.0.bias', 'bbox_predictor.layers.0.bias'),
          ('bbox_embed.layers.1.weight', 'bbox_predictor.layers.1.weight'),
          ('bbox_embed.layers.1.bias', 'bbox_predictor.layers.1.bias'),
          ('bbox_embed.layers.2.weight', 'bbox_predictor.layers.2.weight'),
          ('bbox_embed.layers.2.bias', 'bbox_predictor.layers.2.bias'),
      ]					)

  return rename_keys







def 	lowerCamelCase__			(				_lowerCamelCase			:						Tuple						,							_lowerCamelCase			:						str						,							_lowerCamelCase			:						int					) ->							Union[str, Any]:
  lowerCamelCase_       	=		state_dict.pop(_lowerCamelCase					)
  lowerCamelCase_       	=		val







def 	lowerCamelCase__			(				_lowerCamelCase			:						Tuple						,							_lowerCamelCase			:						List[Any]=False					) ->							Optional[int]:
  lowerCamelCase_       	=		''
  if is_panoptic:
    lowerCamelCase_       	=		'detr.'

  # first: transformer encoder
  for i in range(6					):
    # read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
    lowerCamelCase_       	=		state_dict.pop(F'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight'''					)
    lowerCamelCase_       	=		state_dict.pop(F'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias'''					)
    # next, add query, keys and values (in that order) to the state dict
    lowerCamelCase_       	=		in_proj_weight[:256, :]
    lowerCamelCase_       	=		in_proj_bias[:256]
    lowerCamelCase_       	=		in_proj_weight[256:512, :]
    lowerCamelCase_       	=		in_proj_bias[256:512]
    lowerCamelCase_       	=		in_proj_weight[-256:, :]
    lowerCamelCase_       	=		in_proj_bias[-256:]
  # next: transformer decoder (which is a bit more complex because it also includes cross-attention)
  for i in range(6					):
    # read in weights + bias of input projection layer of self-attention
    lowerCamelCase_       	=		state_dict.pop(F'''{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight'''					)
    lowerCamelCase_       	=		state_dict.pop(F'''{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_bias'''					)
    # next, add query, keys and values (in that order) to the state dict
    lowerCamelCase_       	=		in_proj_weight[:256, :]
    lowerCamelCase_       	=		in_proj_bias[:256]
    lowerCamelCase_       	=		in_proj_weight[256:512, :]
    lowerCamelCase_       	=		in_proj_bias[256:512]
    lowerCamelCase_       	=		in_proj_weight[-256:, :]
    lowerCamelCase_       	=		in_proj_bias[-256:]
    # read in weights + bias of input projection layer of cross-attention
    lowerCamelCase_       	=		state_dict.pop(
        F'''{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight'''					)
    lowerCamelCase_       	=		state_dict.pop(F'''{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_bias'''					)
    # next, add query, keys and values (in that order) of cross-attention to the state dict
    lowerCamelCase_       	=		in_proj_weight_cross_attn[:256, :]
    lowerCamelCase_       	=		in_proj_bias_cross_attn[:256]
    lowerCamelCase_       	=		in_proj_weight_cross_attn[256:512, :]
    lowerCamelCase_       	=		in_proj_bias_cross_attn[256:512]
    lowerCamelCase_       	=		in_proj_weight_cross_attn[-256:, :]
    lowerCamelCase_       	=		in_proj_bias_cross_attn[-256:]







def 	lowerCamelCase__			(				) ->							int:
  lowerCamelCase_       	=		'http://images.cocodataset.org/val2017/000000039769.jpg'
  lowerCamelCase_       	=		Image.open(requests.get(_lowerCamelCase						,							stream=_lowerCamelCase					).raw					)

  return im







@torch.no_grad()
def 	lowerCamelCase__			(				_lowerCamelCase			:						Tuple						,							_lowerCamelCase			:						str=None						,							_lowerCamelCase			:						Union[str, Any]=False					) ->							Any:
  lowerCamelCase_	,      lowerCamelCase_       	=		get_detr_config(_lowerCamelCase					)

  # load original model from torch hub
  lowerCamelCase_       	=		{
      'detr-resnet-50': 'detr_resnet50',
      'detr-resnet-101': 'detr_resnet101',
  }
  logger.info(F'''Converting model {model_name}...'''					)
  lowerCamelCase_       	=		torch.hub.load('facebookresearch/detr'						,							model_name_to_original_name[model_name]						,							pretrained=_lowerCamelCase					).eval()
  lowerCamelCase_       	=		detr.state_dict()
  # rename keys
  for src, dest in create_rename_keys(_lowerCamelCase					):
    if is_panoptic:
      lowerCamelCase_       	=		'detr.' + src
    rename_key(_lowerCamelCase						,							_lowerCamelCase						,							_lowerCamelCase					)
  # query, key and value matrices need special treatment
  read_in_q_k_v(_lowerCamelCase						,							is_panoptic=_lowerCamelCase					)
  # important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
  lowerCamelCase_       	=		'detr.model.' if is_panoptic else 'model.'
  for key in state_dict.copy().keys():
    if is_panoptic:
      if (
          key.startswith('detr'					)
          and not key.startswith('class_labels_classifier'					)
          and not key.startswith('bbox_predictor'					)
      ):
        lowerCamelCase_       	=		state_dict.pop(_lowerCamelCase					)
        lowerCamelCase_       	=		val
      elif "class_labels_classifier" in key or "bbox_predictor" in key:
        lowerCamelCase_       	=		state_dict.pop(_lowerCamelCase					)
        lowerCamelCase_       	=		val
      elif key.startswith('bbox_attention'					) or key.startswith('mask_head'					):
        continue
      else:
        lowerCamelCase_       	=		state_dict.pop(_lowerCamelCase					)
        lowerCamelCase_       	=		val
    else:
      if not key.startswith('class_labels_classifier'					) and not key.startswith('bbox_predictor'					):
        lowerCamelCase_       	=		state_dict.pop(_lowerCamelCase					)
        lowerCamelCase_       	=		val

    # finally, create HuggingFace model and load state dict
  lowerCamelCase_       	=		DetrForSegmentation(_lowerCamelCase					) if is_panoptic else DetrForObjectDetection(_lowerCamelCase					)
  model.load_state_dict(_lowerCamelCase					)
  model.eval()

  # verify our conversion on an image
  lowerCamelCase_       	=		'coco_panoptic' if is_panoptic else 'coco_detection'
  lowerCamelCase_       	=		DetrImageProcessor(format=_lowerCamelCase					)

  lowerCamelCase_       	=		processor(images=prepare_img()						,							return_tensors='pt'					)
  lowerCamelCase_       	=		encoding['pixel_values']

  lowerCamelCase_       	=		detr(_lowerCamelCase					)
  lowerCamelCase_       	=		model(_lowerCamelCase					)

  assert torch.allclose(outputs.logits						,							original_outputs['pred_logits']						,							atol=1e-3					)
  assert torch.allclose(outputs.pred_boxes						,							original_outputs['pred_boxes']						,							atol=1e-3					)
  if is_panoptic:
    assert torch.allclose(outputs.pred_masks						,							original_outputs['pred_masks']						,							atol=1e-4					)
  print('Looks ok!'					)

  if pytorch_dump_folder_path is not None:
    # Save model and image processor
    logger.info(F'''Saving PyTorch model and image processor to {pytorch_dump_folder_path}...'''					)
    Path(_lowerCamelCase					).mkdir(exist_ok=_lowerCamelCase					)
    model.save_pretrained(_lowerCamelCase					)
    processor.save_pretrained(_lowerCamelCase					)

  if push_to_hub:
    # Upload model and image processor to the hub
    logger.info('Uploading PyTorch model and image processor to the hub...'					)
    model.push_to_hub(F'''nielsr/{model_name}'''					)
    processor.push_to_hub(F'''nielsr/{model_name}'''					)


if __name__ == "__main__":
  _SCREAMING_SNAKE_CASE   :							List[Any]          =		argparse.ArgumentParser()

  parser.add_argument(
      '''--model_name''',
      default='''detr-resnet-50''',
      type=str,
      choices=['''detr-resnet-50''', '''detr-resnet-101'''],
      help='''Name of the DETR model you\'d like to convert.''',
  )
  parser.add_argument(
      '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the folder to output PyTorch model.'''
  )
  parser.add_argument('''--push_to_hub''', action='''store_true''', help='''Whether to push the model to the hub or not.''')
  _SCREAMING_SNAKE_CASE   :							List[Any]          =		parser.parse_args()
  convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)












"""simple docstring"""





import functools
from typing import Any







def 	lowerCamelCase__			(				_lowerCamelCase			:						str						,							_lowerCamelCase			:						list[str]					) ->							bool:

  # Validation
  if not isinstance(_lowerCamelCase						,							_lowerCamelCase					) or len(_lowerCamelCase					) == 0:
    raise ValueError('the string should be not empty string'					)

  if not isinstance(_lowerCamelCase						,							_lowerCamelCase					) or not all(
      isinstance(_lowerCamelCase						,							_lowerCamelCase					) and len(_lowerCamelCase					) > 0 for item in words					):
    raise ValueError('the words should be a list of non-empty strings'					)

  # Build trie
  lowerCamelCase_       	=		{}
  lowerCamelCase_       	=		'WORD_KEEPER'

  for word in words:
    lowerCamelCase_       	=		trie
    for c in word:
      if c not in trie_node:
        lowerCamelCase_       	=		{}

      lowerCamelCase_       	=		trie_node[c]

    lowerCamelCase_       	=		True

  lowerCamelCase_       	=		len(_lowerCamelCase					)

  # Dynamic programming method
  @functools.cache
  def is_breakable(_lowerCamelCase			:						int					) -> bool:
    if index == len_string:
      return True

    lowerCamelCase_       	=		trie
    for i in range(_lowerCamelCase						,							_lowerCamelCase					):
      lowerCamelCase_       	=		trie_node.get(string[i]						,							_lowerCamelCase					)

      if trie_node is None:
        return False

      if trie_node.get(_lowerCamelCase						,							_lowerCamelCase					) and is_breakable(i + 1					):
        return True

    return False

  return is_breakable(0					)


if __name__ == "__main__":
  import doctest

  doctest.testmod()




